feat: add brevitas channel-wise support

conftest.py

@@ -60,7 +60,10 @@ def pytest_addoption(parser):
     )
 
     parser.addoption(
-        "--no-flaky", action="store_true", default=False, help="Don't run known flaky tests."
+        "--no-flaky",
+        action="store_true",
+        default=False,
+        help="Don't run known flaky tests.",
     )
 
 
@@ -382,8 +385,12 @@ def check_float_array_equal_impl(
         a, b, rtol=0, atol=0.001, error_information: Optional[str] = ""
     ):
 
+        max_atol = numpy.abs(a - b).max()
+        max_rtol = (numpy.abs(a - b) / numpy.abs(b)).max()
+
         error_message = (
             f"Not equal to tolerance rtol={rtol}, atol={atol}\na: {a}\nb: {b}\n"
+            f"Found {max_atol=}, {max_rtol=}\n"
             f"{error_information}"
         )
 

deps_licenses/licenses_mac_silicon_user.txt

@@ -1,18 +1,20 @@
 Name, Version, License
+Jinja2, 3.1.4, BSD License
+MarkupSafe, 2.1.5, BSD License
 PyYAML, 6.0.1, MIT License
-brevitas, 0.8.0, UNKNOWN
-certifi, 2024.6.2, Mozilla Public License 2.0 (MPL 2.0)
+brevitas, 0.10.2, UNKNOWN
+certifi, 2024.7.4, Mozilla Public License 2.0 (MPL 2.0)
 charset-normalizer, 3.3.2, MIT License
 coloredlogs, 15.0.1, MIT License
 concrete-python, 2.7.0, BSD-3-Clause
 dependencies, 2.0.1, BSD License
 dill, 0.3.8, BSD License
-filelock, 3.15.3, The Unlicense (Unlicense)
+filelock, 3.15.4, The Unlicense (Unlicense)
 flatbuffers, 24.3.25, Apache Software License
-fsspec, 2024.6.0, BSD License
+fsspec, 2024.6.1, BSD License
 huggingface-hub, 0.23.4, Apache Software License
 humanfriendly, 10.0, MIT License
-hummingbird-ml, 0.4.8, MIT License
+hummingbird-ml, 0.4.11, MIT License
 idna, 3.7, BSD License
 importlib_resources, 6.4.0, Apache Software License
 joblib, 1.4.2, BSD License
@@ -22,7 +24,7 @@ networkx, 3.1, BSD License
 numpy, 1.23.5, BSD License
 onnx, 1.16.1, Apache License v2.0
 onnxconverter-common, 1.13.0, MIT License
-onnxmltools, 1.11.0, Apache Software License
+onnxmltools, 1.12.0, Apache Software License
 onnxoptimizer, 0.3.13, Apache License v2.0
 onnxruntime, 1.18.0, MIT License
 packaging, 24.1, Apache Software License; BSD License
@@ -35,16 +37,18 @@ requests, 2.32.3, Apache Software License
 scikit-learn, 1.1.3, BSD License
 scipy, 1.10.1, BSD License
 six, 1.16.0, MIT License
-skl2onnx, 1.12, Apache Software License
+skl2onnx, 1.17.0, Apache Software License
 skops, 0.5.0, MIT
 skorch, 0.11.0, new BSD 3-Clause
-sympy, 1.12.1, BSD License
+sympy, 1.13.0, BSD License
 tabulate, 0.8.10, MIT License
 threadpoolctl, 3.5.0, BSD License
-torch, 1.13.1, BSD License
+torch, 2.3.1, BSD License
 tqdm, 4.66.4, MIT License; Mozilla Public License 2.0 (MPL 2.0)
-typing_extensions, 4.5.0, Python Software Foundation License
+typing_extensions, 4.12.2, Python Software Foundation License
 tzdata, 2024.1, Apache Software License
+unfoldNd, 0.2.2, MIT License
 urllib3, 2.2.2, MIT License
 xgboost, 1.6.2, Apache Software License
 z3-solver, 4.13.0.0, MIT License
+zipp, 3.19.2, MIT License

deps_licenses/licenses_mac_silicon_user.txt.md5

@@ -1 +1 @@
-adb925c3b7be3e651975febcf49b6543
+6d367701c3ef5eff8763f4e994e03681

src/concrete/ml/onnx/convert.py

@@ -86,7 +86,11 @@ def fuse_matmul_bias_to_gemm(onnx_model: onnx.ModelProto):
         # Create a GEMM node which combines the MatMul and Add operations
         gemm_node = helper.make_node(
             "Gemm",  # op_type
-            [matmul_node.input[0], matmul_node.input[1], bias_other_input_node_name],  # inputs
+            [
+                matmul_node.input[0],
+                matmul_node.input[1],
+                bias_other_input_node_name,
+            ],  # inputs
             [add_node.output[0]],  # outputs
             name="Gemm_Node",
             alpha=1.0,
@@ -149,9 +153,14 @@ def get_equivalent_numpy_forward_from_torch(
 
     arguments = list(inspect.signature(torch_module.forward).parameters)
 
+    if isinstance(dummy_input, torch.Tensor):
+        dummy_input = dummy_input.to("cpu")
+    else:
+        dummy_input = tuple(elt.to("cpu") for elt in dummy_input)
+
     # Export to ONNX
     torch.onnx.export(
-        torch_module,
+        torch_module.to("cpu"),
         dummy_input,
         str(output_onnx_file_path),
         opset_version=OPSET_VERSION_FOR_ONNX_EXPORT,